[1]} showed that modern deep neural networks are miscalibrated. This implies that the model's confidence in its estimate is not reflective of its accuracy. Typically, the output after a softmax layer of a neural network is interpreted as confidence [2]}, [1]}. Many studies have found that DNNs output high confidences for incorrectly classified samples [1]}, [5]}. For scenarios such as automated driving, medical image analysis etc. where one wishes to avoid failures at all cost, such highly confident incorrect predictions can prove fatal. As a result, calibration is a desired property of the deployed neural networks, which is being actively studied in deep learning research. However, calibration is not the only component that describes a reliable system. Along with calibration we also require the predictions to be refined.
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